Build SLO configurations for Cloud Monitoring with request-based SLIs, burn rate alerts, and error budget tracking.
Last verified: May 2026
Build SLO configurations for Cloud Monitoring with request-based SLIs, burn rate alerts, and error budget tracking.
Required Fields
serviceNameslo.displayNameslo.goalslo.serviceLevelIndicatorOutput will appear here...Service Level Objectives (SLOs) in Cloud Monitoring let you define and track reliability targets for your services based on measurable Service Level Indicators (SLIs) like availability, latency, and throughput. SLOs integrate with error budgets and burn-rate alerts to help you balance reliability investment with feature velocity. This builder helps you configure SLO definitions with compliance targets, rolling or calendar windows, SLI methods (request-based or window-based), and burn-rate alerting thresholds.
Your team's customer-facing API has no formal SLO — leadership wants reliability targets. The builder generates: a 99.9% availability SLO over a 28-day rolling window, with burn-rate alerts at 14x rate (1-hour window, fires after ~5 minutes of failures) and 6x rate (6-hour window, fires after slow degradation). Documentation links to the incident runbook. Fast burns page on-call immediately; slow burns notify Slack for investigation. After 6 months, the team has hard data on reliability trends and an objective basis for prioritizing reliability work.
Burn-rate alerts at multiple time windows are dramatically more useful than single threshold alerts. A '14x burn over 1 hour AND 6x over 6 hours' multi-window alert catches both fast outages and slow degradation. Single-window alerts either miss slow burns or fire on noise.
Don't set SLO targets unrealistically high (99.99%+ for greenfield services). Start with achievable targets (99.5%) and tighten over time as the service matures. SLOs you can't meet just create constant alert noise without driving improvement. Match SLO ambition to engineering capacity.
Error budgets are a feature velocity tool, not a punishment tool. When budget is exhausted, the team focuses on reliability. When budget is healthy, the team can take risks (faster releases, experiments). This balance is the SRE philosophy's core insight — operationalize it via budget-driven feature freeze policies.
The builder constructs SLO definitions with: service association (Cloud Run service, GKE workload, custom service), SLI specification (request-based with good_service_filter / total_service_filter, OR window-based with availability/latency/throughput conditions), goal target (e.g., 0.999 = 99.9%), period (rolling N days, or calendar window), and burn-rate alert thresholds (multi-window, multi-burn-rate). Output is gcloud monitoring slos commands and Terraform google_monitoring_slo + google_monitoring_alert_policy resources.
Was this tool helpful?
Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.